企業基於業務需求會蒐集大量的個人資料。近來，企業基於成本考量往往會將資料委外儲存到雲端資料庫服務中，並自行建置資料查詢介面給使用者。但將資料委外到雲端資料庫服務中，雲端資料庫服務提供者便可能侵犯資料擁有者隱私。此外，資料查詢介面也必須根據不同使用情境提供不同揭露程度的資料給使用者，像是基於診療目的的醫生應該使用完整的資料，而醫療研究人員便只能使用匿名處理的資料。如何根據使用情境提供不同揭露程度的資料來確保資料使用上的隱私保護是一個重要的議題。
本研究將探討結構化單一資料源的資料委外和委外資料使用時的隱私保護，藉由在資料委外前以加密結合分割的方式處理資料，以及運用OWL本體論和LP規則語言設計三種規範：Access Control Policy (ACP)、Data Handling Policy (DHP)和Data Releasing Policy (DRP)，並且以規則語言來落實規範。透過這三種規範的合作和分工來提供揭露程度不同的資料確保資料委外和使用上的隱私保護。

Enterprise collects a great amount of personal identifiable information (PII) for business service. Recently, these PII data are outsourced for Database-as-a-Service (DaaS) in the cloud to reduce the enterprise's data administration cost. However, this might provide an opportunity for cloud service providers (CSPs) to infringe data owners' privacy. In addition, a data controller of enterprise should provide an interface for data users with different level of data usage based on its access context. For example, a doctor can use a patient's complete PII when (s)he performs the medication practice. On the other hand, a researcher for medical analysis can only use de-identifiable PII. Therefore, the research challenge is to ensure how privacy protection criteria are satisfied for various data disclosure when using associated data usage context.
In this study, we address the research problem for outsourcing data protection from a single data source in the cloud. We apply encryption and fragmentation techniques for these outsourcing data to avoid privacy violation risk from CSPs. Furthermore, we use OWL-based ontologies to represent there types of data protection policies, i.e., Access Control Policy (ACP), Data Handling Policy (DHP), and Data Releasing Policy (DRP). In addition, we use Logic-Program (LP) rules to enforce these policies. Finally, through integration and collaboration of these policies, we ensure the satisfaction of privacy protection criteria and achieve different level of outsourcing data disclosure in the cloud.

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